scholarly journals Construction of a Computer-Aided Analysis System for Orthopedic Diseases Based on High-Frequency Ultrasound Images

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Feifei Xiu ◽  
Guishan Rong ◽  
Tao Zhang
Keyword(s):  
Random Forest ◽  
Wiener Filter ◽  
Principal Component ◽  
Random Forest Classifier ◽  
Imaging Method ◽  
Ultrasound Images ◽  
Segmentation Method ◽  
High Frequency Ultrasound ◽  
Computer Aided ◽  
Orthopedic Diseases

The area of medical diagnosis has been transformed by computer-aided diagnosis (CAD). With the advancement of technology and the widespread availability of medical data, CAD has gotten a lot of attention, and numerous methods for predicting different pathological diseases have been created. Ultrasound (US) is the safest clinical imaging method; therefore, it is widely utilized in medical and healthcare settings with computer-aided systems. However, owing to patient movement and equipment constraints, certain artefacts make identification of these US pictures challenging. To enhance the quality of pictures for classification and segmentation, certain preprocessing techniques are required. Hence, we proposed a three-stage image segmentation method using U-Net and Iterative Random Forest Classifier (IRFC) to detect orthopedic diseases in ultrasound images efficiently. Initially, the input dataset is preprocessed using Enhanced Wiener Filter for image denoising and image enhancement. Then, the proposed segmentation method is applied. Feature extraction is performed by transform-based analysis. Finally, obtained features are reduced to optimal subset using Principal Component Analysis (PCA). The classification is done using the proposed Iterative Random Forest Classifier. The proposed method is compared with the conventional performance measures like accuracy, specificity, sensitivity, and dice score. The proposed method is proved to be efficient for detecting orthopedic diseases in ultrasound images than the conventional methods.

scholarly journals Machine learning based prognostic model for predicting infection susceptibility of COVID-19 using health care data

2020 ◽  
Author(s):  
R Srivat ◽  
Prithviraj N Indi ◽  
Swapnil Agrahari ◽  
Siddharth Menon ◽  
S. Denis Ashok
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Random Forest ◽  
Support Vector Regression ◽  
Prognostic Model ◽  
Principal Component ◽  
Random Forest Classifier ◽  
Support Vector ◽  
Data Set ◽  
Health Care Data

Abstract From public health perspectives of COVID-19 pandemic, accurate estimates of infection severity of individuals are extremely valuable for the informed decision making and targeted response to an emerging pandemic. This paper presents machine learning based prognostic model for providing early warning to the individuals for COVID-19 infection using the health care data set. In the present work, a prognostic model using Random Forest classifier and support vector regression is developed for predicting the susceptibility of COVID-19 infection and it is applied on an open health care data set containing 27 field values. The typical fields of the health care data set include basic personal details such as age, gender, number of children in the household, marital status along with medical data like Coma score, Pulmonary score, Blood Glucose level, HDL cholesterol etc. An effective preprocessing method is carried out for handling the numerical, categorical values (non-numerical), missing data in the health care data set. Principal component analysis is applied for dimensionality reduction of the health care data set. From the classification results, it is noted that the random forest classifier provides a higher accuracy as compared to Support vector regression for the given health data set. Proposed machine learning approach can help the individuals to take additional precautions for protecting against COVID-19 infection. Based on the results of the proposed method, clinicians and government officials can focus on the highly susceptible people for limiting the pandemic spread. Methods In the present work, Random Forest classifier and support vector regression techniques are applied to a medical health care dataset containing 27 variables for predicting the susceptibility score of an individual towards COVID-19 infection and the accuracy of prediction is compared. An effective preprocessing is carried for handling the missing data in the health care data set. Principal Component Analysis is carried out on the data set for dimensionality reduction of the feature vectors. Results From the classification results, it is noted that the Random Forest classifier provides an accuracy of 90%, sensitivity of 94% and specificity of 81% for the given medical data set.Conclusion Proposed machine learning approach can help the individuals to take additional precautions for protecting people from the COVID-19 infection, clinicians and government officials can focus on the highly susceptible people for limiting the pandemic spread.

scholarly journals Segmentation of Mammary Lesions in Ultrasound Images Applying Mask R-CNN

2019 ◽  
Author(s):  
Claudia Raquel Ibarrola Chamorro ◽  
Wagner Coelho de Albuquerque Pereira
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Fundamental Aspect ◽  
Ultrasound Images ◽  
Segmentation Method ◽  
Cad Systems ◽  
Cad System ◽  
Road Signs ◽  
The World ◽  
Computer Aided

Breast cancer is the most frequent malignant tumor in women and one of the most common in the world. One of the most important issues in this condition is early detection. Computer-aided diagnostic (CAD) systems are objects of research, aiming to provide a second opinion to the health professional. A fundamental aspect within the CAD system is the segmentation of the lesion, allowing an adequate extraction of the lesion characteristics. The use of a computerized segmentation method helps eliminate human variability and, consequently, improve the performance of the lesion classifier. Convolutional Neural Networks (CNNs) are being used in segmentation problems, such as various types of medical imaging, people and road signs detection, for example. So inspired by these promissing results, the present work has as main objective to analyze and implement the Mask R-CNN as a tool of segmentation of mammary lesions in images obtained by ultrasound, to propose an efficient method of segmentation, aiding in the classification process of the CAD systems.

scholarly journals Feature Extraction and Classification for the Detection of Knee Joint Disorders using Random Forest Classifier

2021 ◽  
Vol 9 (10) ◽  
pp. 1348-1356
Keyword(s):  
Principal Component Analysis ◽  
Feature Selection ◽  
Random Forest ◽  
Knee Joint ◽  
Principal Component ◽  
Approximate Entropy ◽  
Component Analysis ◽  
Random Forest Classifier ◽  
Invasive Technique ◽  
Entropy Measures

A non-invasive technique using knee joint vibroarthrographic (VAG) signals can be used for the early diagnosis of knee joint disorders. Among the algorithms devised for the detection of knee joint disorders using VAG signals, algorithms based on entropy measures can provide better performance. In this work, the VAG signal is preprocessed using wavelet decomposition into sub band signals. Features of the decomposed sub bands such as approximate entropy, sample entropy and wavelet energy are extracted as a quantified measure of complexity of the signal. A feature selection based on Principal Component Analysis (PCA) is performed in order to select the significant features. The extracted features are then used for classification of VAG signal into normal and abnormal VAG using random forest classifier. It is observed that the classifier provides a better accuracy with feature selection using principal component analysis. And the result shows that the classifier is able to classify the signal with an accuracy of 87%, error rate of 0.13, sensitivity of 0.874 and specificity of 0.777.

The Performance Improvement on 4-FB Face using Random Forest Classifier

2018 ◽  
Vol 10 (5) ◽  
pp. 1-12
Author(s):  
B. Nassih ◽  
A. Amine ◽  
M. Ngadi ◽  
D. Naji ◽  
N. Hmina
Keyword(s):  
Random Forest ◽  
Performance Improvement ◽  
Random Forest Classifier

Methodology for Malware Classification using a Random Forest Classifier

2018 ◽  
Author(s):  
Carlos Domenick Morales-Molina ◽  
Diego Santamaria-Guerrero ◽  
Gabriel Sanchez-Perez ◽  
Hector Perez-Meana ◽  
Aldo Hernandez-Suarez
Keyword(s):  
Random Forest ◽  
Random Forest Classifier ◽  
Malware Classification

scholarly journals Transformer Oil Quality Assessment Using Random Forest with Feature Engineering

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1809
Author(s):  
Mohammed El Amine Senoussaoui ◽  
Mostefa Brahami ◽  
Issouf Fofana
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Oil Quality ◽  
Principal Component ◽  
Condition Assessment ◽  
Classification Performance ◽  
Transformer Oil ◽  
Classification Model ◽  
Insulation Degradation ◽  
Transformer Oils

Machine learning is widely used as a panacea in many engineering applications including the condition assessment of power transformers. Most statistics attribute the main cause of transformer failure to insulation degradation. Thus, a new, simple, and effective machine-learning approach was proposed to monitor the condition of transformer oils based on some aging indicators. The proposed approach was used to compare the performance of two machine-learning classifiers: J48 decision tree and random forest. The service-aged transformer oils were classified into four groups: the oils that can be maintained in service, the oils that should be reconditioned or filtered, the oils that should be reclaimed, and the oils that must be discarded. From the two algorithms, random forest exhibited a better performance and high accuracy with only a small amount of data. Good performance was achieved through not only the application of the proposed algorithm but also the approach of data preprocessing. Before feeding the classification model, the available data were transformed using the simple k-means method. Subsequently, the obtained data were filtered through correlation-based feature selection (CFsSubset). The resulting features were again retransformed by conducting the principal component analysis and were passed through the CFsSubset filter. The transformation and filtration of the data improved the classification performance of the adopted algorithms, especially random forest. Another advantage of the proposed method is the decrease in the number of the datasets required for the condition assessment of transformer oils, which is valuable for transformer condition monitoring.

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